Study of human-machine physical interface for wearable mobility assist devices
Bibliographic record
Abstract
A decrease in mobility, related to illness, trauma or ageing, negatively affects the quality of life of the rapidly growing elderly population. A promising solution to maintain this standard of living is powered wearable mobility assist devices. Although they have achieved technological breakthroughs in the last decade, their overall success is still hindered by their induced physical discomfort, which limits their effective and prolonged usage. The aim of this study is to achieve a comprehensive characterization of human-machine physical interface to further advance the performance of wearable mobility assist devices, specifically for the knee joint. This led the research group to design, fabricate, and instrument a low-cost modular knee orthosis testing apparatus with extension moment assist that allows multiple physical interface adjustment parameters. This device was conceived with the objective to conduct human testing while introducing design variables and operating parameters to evaluate device's performance. Using a force mapping apparatus and a motion capture system, the kinetic and the kinematic behaviour of the developed orthosis' physical interfaces were acquired. The results demonstrated varied impact on performance when introducing key design variables namely interface position, interface geometry, interface compliancy, interface hard-shell position, interface degree of freedom, and knee extension moment. This study provides an in-depth understanding of distinct user-device interface mechanisms and permitted an evaluation of optimum orthosis parameters to help further advance the state of wearable mobility assist devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".